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arXiv · 2304.10817

Can SAM Count Anything? An Empirical Study on SAM Counting

Zhiheng Ma·Xiaopeng Hong·Qinnan Shangguan
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Abstract

Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects. Code can be found at \url{https://github.com/Vision-Intelligence-and-Robots-Group/count-anything}.

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Zhiheng Ma, Xiaopeng Hong, Qinnan Shangguan. 2023-04-21. Can SAM Count Anything? An Empirical Study on SAM Counting. https://arxiv.org/abs/2304.10817

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